基于加权核范数和L2,1范数的最优均值线性分类器

缺陷检测是智能制造系统的一个重要的环节。在采用传统机器学习算法进行缺陷分类的时候,通常会遇 到数据噪声干扰,降低算法对缺陷类别的预测精度。尽管近几年提出了如鲁棒线性判别分析(RLDA)等强大的算法用于解决数据受稀疏噪声干扰的分类问题,但仍存在一些缺点限制其应用性能。该文提出一种新的基于线性判别分析的最优均值鲁棒线性分类模型(OMRLSA)。不同于以往应对噪声数据的分类方法忽略稀疏噪声具有的拉普拉斯分布特性对数据均值的影响,该文所提出的最优均值鲁棒线性分类模型会自动更新数据的最优均值,从而保证数据的统计特性不会受到噪声的干扰。此外,随后的损失函数中首次在鲁棒分类模型中引入了关于正则化和误差测量的联合L_2,1范数最小化和秩压缩的加权核范数最小化方法,从而提高算法的鲁棒性。在具有不同比例损坏的标准数据集上的实验结果说明了该文方法的优越性。

Optimal Mean Linear Classifier via Weighted Nuclear Norm and L2,1 Norm

Defect detection is an important part of intelligent manufacturing system. When traditional machine learning algorithms are used for defect classification, data noise interference is usually encountered, which reduces the algorithm’s prediction accuracy for defect classification. Although powerful algorithms such as Robust Linear Discriminant Analysis (RLDA) have been proposed in recent years to solve classification problems with data disturbed by sparse noise, there are still some drawbacks that limit its application performance. In this paper, a new Optimal Mean-Robust Linear Classification Analyis (OMRLSA) based on linear discriminant analysis is proposed. Different from the previous classification methods dealing with noisy data, ignoring the influence of the Laplace distribution characteristic of sparse noise on the data mean, the optimal mean robust linear classification model proposed in this paper will automatically update the optimal mean of the data. This ensures that the statistical characteristics of the data will not be disturbed by noise. Furthermore, a weighted kernel norm minimization method with joint L_2,1 norm minimization and rank compression on regularization and error measurement is introduced for the first time in a robust classification model in the subsequent loss function. Thereby the robustness of the algorithm is improved. Experimental results on standard dataset with different ratio corruption illustrate the superiority of the proposed method.